Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "118" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 31 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.808995 | 1.030469 | 4.178129 | 1.354162 | 0.650874 | 0.569206 | -0.005302 | -0.312023 | 0.8416 | 0.6729 | 0.4907 | 3.377856 | 2.928196 |
| 2459845 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459843 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 18.162340 | 19.061673 | 15.291719 | 16.929888 | 64.415081 | 71.069794 | -0.369252 | -0.414930 | 0.0301 | 0.0363 | 0.0056 | 1.171048 | 1.170968 |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.588002 | -0.235800 | 1.117857 | 0.653016 | 1.217803 | 0.346862 | -2.018898 | -2.977530 | 0.0247 | 0.0246 | 0.0008 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 0.019963 | -0.349465 | 14.269714 | 12.700473 | 5.600283 | 3.244831 | 6.487280 | 3.098287 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 16.776020 | 19.170352 | 17.987269 | 19.867240 | 21.706991 | 31.545622 | -0.220042 | -0.367226 | 0.0293 | 0.0331 | 0.0025 | 1.236384 | 1.259185 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0472 | 0.0957 | 0.0111 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.305481 | 1.917891 | 0.937175 | 0.847889 | -0.760713 | -1.146657 | -0.390397 | -0.022096 | 0.0494 | 0.0933 | 0.0138 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.694237 | 4.733696 | 2.104733 | -0.162163 | 0.073586 | -0.467681 | -0.575265 | -0.905029 | 0.0658 | 0.0842 | 0.0163 | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.743131 | 39.420295 | 1.744922 | 22.356034 | 0.949944 | 12.477860 | 1.071661 | -0.162268 | 0.8185 | 0.0496 | 0.6093 | 3.801543 | 1.320973 |
| 2459831 | digital_ok | 100.00% | 100.00% | 44.89% | 0.00% | - | - | 1.216905 | 1.177736 | 18.403604 | 16.394252 | 7.454913 | 5.892588 | -1.610701 | -2.662710 | 0.1918 | 0.2656 | 0.0225 | nan | nan |
| 2459830 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.820747 | 38.223801 | 2.506528 | 31.843548 | -0.564185 | 36.475544 | 2.058794 | 0.980446 | 0.8207 | 0.0478 | 0.5038 | 4.754117 | 1.342757 |
| 2459829 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 5.127107 | 38.310607 | 1.697722 | 25.318606 | -0.749940 | 32.997746 | 1.583298 | 0.753700 | 0.7580 | 0.0501 | 0.4587 | 17.085589 | 1.349106 |
| 2459828 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.444997 | 31.984556 | 1.137538 | 27.940176 | -0.961446 | 33.596489 | -0.519601 | 4.428474 | 0.8164 | 0.0502 | 0.5223 | 4.769563 | 1.245459 |
| 2459827 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.440443 | 29.505855 | 2.769368 | 31.025237 | -0.031438 | 26.692914 | -0.193162 | -0.948185 | 0.7554 | 0.0484 | 0.4107 | 8.400148 | 1.270718 |
| 2459826 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.186042 | 29.029873 | 2.950854 | 35.174598 | -0.926287 | 45.570619 | -0.215537 | 3.069430 | 0.8065 | 0.0491 | 0.4893 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.753405 | 31.518340 | 1.634076 | 28.282420 | -1.098361 | 25.789033 | -0.722465 | -0.310416 | 0.8141 | 0.0493 | 0.4015 | 4.835412 | 1.268158 |
| 2459824 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 1.184588 | 22.073296 | 2.158959 | 21.811973 | -0.552542 | 19.548152 | 0.177300 | -0.025019 | 0.7128 | 0.0481 | 0.3180 | 5.430461 | 1.321672 |
| 2459823 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.541613 | 27.536644 | 2.933803 | 42.490663 | 1.606483 | 36.032293 | -0.468575 | 25.210659 | 0.7663 | 0.0487 | 0.3771 | 54.166641 | 1.333457 |
| 2459822 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.686601 | 29.773009 | 2.296176 | 38.562312 | -0.447120 | 29.723044 | 0.106859 | -0.103968 | 0.8033 | 0.0503 | 0.4122 | 4.809486 | 1.273519 |
| 2459821 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.021764 | 33.366638 | 2.266414 | 39.192383 | -0.729142 | 26.038175 | -1.170894 | -1.227518 | 0.8092 | 0.0442 | 0.4662 | 4.885820 | 1.238810 |
| 2459820 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 4.185083 | 29.146308 | 2.447325 | 31.061740 | -0.474223 | 70.714619 | 2.503867 | 0.635577 | 0.7631 | 0.0517 | 0.4791 | 4.403567 | 1.300430 |
| 2459817 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.771247 | 28.679154 | 1.557459 | 38.392574 | 0.085004 | 36.772078 | 0.116977 | 0.704878 | 0.8112 | 0.0532 | 0.5227 | 4.057866 | 1.321839 |
| 2459816 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.253228 | 23.599998 | 2.741401 | 38.498190 | 1.560346 | 45.704915 | 1.076637 | 2.525416 | 0.8541 | 0.0520 | 0.6463 | 5.231166 | 1.270027 |
| 2459815 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.101329 | 25.153446 | 2.568122 | 42.247765 | 1.947453 | 48.235579 | -0.514039 | 7.100405 | 0.8174 | 0.0499 | 0.5914 | 5.196134 | 1.238075 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 5.368763 | 30.596226 | 1.808102 | 25.785123 | 1.015559 | 87.067970 | 3.131879 | 2.187284 | 0.7815 | 0.0440 | 0.3928 | 12.009362 | 1.363353 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | ee Power | 4.178129 | -0.808995 | 1.030469 | 4.178129 | 1.354162 | 0.650874 | 0.569206 | -0.005302 | -0.312023 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 71.069794 | 19.061673 | 18.162340 | 16.929888 | 15.291719 | 71.069794 | 64.415081 | -0.414930 | -0.369252 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | ee Temporal Variability | 1.217803 | -0.588002 | -0.235800 | 1.117857 | 0.653016 | 1.217803 | 0.346862 | -2.018898 | -2.977530 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | ee Power | 14.269714 | -0.349465 | 0.019963 | 12.700473 | 14.269714 | 3.244831 | 5.600283 | 3.098287 | 6.487280 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 31.545622 | 19.170352 | 16.776020 | 19.867240 | 17.987269 | 31.545622 | 21.706991 | -0.367226 | -0.220042 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 1.917891 | 1.917891 | -0.305481 | 0.847889 | 0.937175 | -1.146657 | -0.760713 | -0.022096 | -0.390397 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 4.733696 | 4.733696 | -0.694237 | -0.162163 | 2.104733 | -0.467681 | 0.073586 | -0.905029 | -0.575265 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 39.420295 | 2.743131 | 39.420295 | 1.744922 | 22.356034 | 0.949944 | 12.477860 | 1.071661 | -0.162268 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | ee Power | 18.403604 | 1.216905 | 1.177736 | 18.403604 | 16.394252 | 7.454913 | 5.892588 | -1.610701 | -2.662710 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 38.223801 | 2.820747 | 38.223801 | 2.506528 | 31.843548 | -0.564185 | 36.475544 | 2.058794 | 0.980446 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 38.310607 | 38.310607 | 5.127107 | 25.318606 | 1.697722 | 32.997746 | -0.749940 | 0.753700 | 1.583298 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 33.596489 | 31.984556 | 2.444997 | 27.940176 | 1.137538 | 33.596489 | -0.961446 | 4.428474 | -0.519601 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Power | 31.025237 | 2.440443 | 29.505855 | 2.769368 | 31.025237 | -0.031438 | 26.692914 | -0.193162 | -0.948185 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 45.570619 | 29.029873 | 2.186042 | 35.174598 | 2.950854 | 45.570619 | -0.926287 | 3.069430 | -0.215537 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 31.518340 | 31.518340 | 1.753405 | 28.282420 | 1.634076 | 25.789033 | -1.098361 | -0.310416 | -0.722465 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | 22.073296 | 1.184588 | 22.073296 | 2.158959 | 21.811973 | -0.552542 | 19.548152 | 0.177300 | -0.025019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Power | 42.490663 | 27.536644 | 2.541613 | 42.490663 | 2.933803 | 36.032293 | 1.606483 | 25.210659 | -0.468575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Power | 38.562312 | 2.686601 | 29.773009 | 2.296176 | 38.562312 | -0.447120 | 29.723044 | 0.106859 | -0.103968 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Power | 39.192383 | 33.366638 | 3.021764 | 39.192383 | 2.266414 | 26.038175 | -0.729142 | -1.227518 | -1.170894 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 70.714619 | 4.185083 | 29.146308 | 2.447325 | 31.061740 | -0.474223 | 70.714619 | 2.503867 | 0.635577 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Power | 38.392574 | 2.771247 | 28.679154 | 1.557459 | 38.392574 | 0.085004 | 36.772078 | 0.116977 | 0.704878 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 45.704915 | 23.599998 | 3.253228 | 38.498190 | 2.741401 | 45.704915 | 1.560346 | 2.525416 | 1.076637 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 48.235579 | 25.153446 | 3.101329 | 42.247765 | 2.568122 | 48.235579 | 1.947453 | 7.100405 | -0.514039 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 118 | N07 | digital_ok | nn Temporal Variability | 87.067970 | 30.596226 | 5.368763 | 25.785123 | 1.808102 | 87.067970 | 1.015559 | 2.187284 | 3.131879 |